Automatic spoken language identification based on phoneme sequence patterns

ABSTRACT

A language identification system that includes a universal phoneme decoder (UPD) is described. The UPD contains a universal phoneme set representing both 1) all phonemes occurring in the set of two or more spoken languages, and 2) captures phoneme correspondences across languages, such that a set of unique phoneme patterns and probabilities are calculated in order to identify a most likely phoneme occurring each time in the audio files in the set of two or more potential languages in which the UPD was trained on. Each statistical language model (SLM) uses the set of unique phoneme patterns created for each language in the set to distinguish between spoken human languages in the set of languages. The run-time language identifier module identifies a particular human language being spoken by utilizing the linguistic probabilities supplied by the SLMs that are based on the set of unique phoneme patterns created for each language.

RELATED APPLICATIONS

This application is a continuation of and claims the benefit of U.S.patent application Ser. No. 12/535,038, filed 4 Aug. 2009, titled‘Improvements for automatic spoken language identification based onphoneme sequence patterns’ and now U.S. Pat. No. 8,190,420 issued on May29, 2012.

NOTICE OF COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the software engineand its modules, as it appears in the Patent and Trademark Office Patentfile or records, but otherwise reserves all copyright rights whatsoever.

FIELD OF THE INVENTION

Embodiments of the invention generally relate to speech recognition,text compression, language identification and cryptography. Moreparticularly, an aspect of an embodiment of the invention relates tolanguage identification.

BACKGROUND OF THE INVENTION

In continuous speech, it is nearly impossible to predict ahead of timewhen the beginning and ending of words in the stream of continuousspeech will individually begin and stop.

SUMMARY OF THE INVENTION

Various methods and apparatus are described for a languageidentification engine. The language identification engine includes atleast the following components. A front end module that has an inputconfigured to receive an audio stream that corresponds to at least oneof a set of two or more candidate languages being spoken in the audiostream under analysis. A universal phoneme decoder that contains auniversal phoneme set that 1) represents all phonemes occurring in theset of two or more candidate languages, and 2) captures phonemecorrespondences across languages, such that a set of unique phonemepatterns and probabilities are calculated in order to identify a mostlikely phoneme occurring for phonemes in the audio stream in the set oftwo or more candidate languages. One or more statistical language modelshave logic configured to supply to a run-time language identifier moduleprobabilities of how linguistically likely a particular uttered phonemeidentified by the universal phoneme decoder comes from a particularcandidate language based on an identified sequence of phonemes. Therun-time language identifier module identifies a particular humanlanguage being spoken in the received audio stream from the set of twoor more candidate languages by utilizing the one or more statisticallanguage models, which have been trained by the universal phonemedecoder.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings refer to embodiments of the invention in which:

FIG. 1 illustrates a block diagram of a language identification enginein a training phase.

FIG. 2 illustrates a block diagram of a language identification enginein a run-time recognition phase.

FIG. 3 illustrates a block diagram of a continuous speech recognitionengine.

FIG. 4 illustrates an embodiment of a continuous speech recognitionengine with a language identification engine that improves an accuracyof probability estimates.

FIG. 5 illustrates a graph of the continuous speech recognition enginemonitoring and transcribing the phone conversation.

While the invention is subject to various modifications and alternativeforms, specific embodiments thereof have been shown by way of example inthe drawings and will herein be described in detail. The inventionshould be understood to not be limited to the particular formsdisclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the invention.

DETAILED DISCUSSION

In the following description, numerous specific details are set forth,such as examples of specific data signals, named components,connections, types of formulas, etc., in order to provide a thoroughunderstanding of the present invention. It will be apparent, however, toone of ordinary skill in the art that the present invention may bepracticed without these specific details. In other instances, well knowncomponents or methods have not been described in detail but rather in ablock diagram in order to avoid unnecessarily obscuring the presentinvention. Further specific numeric references such as first input, maybe made. However, the specific numeric reference should not beinterpreted as a literal sequential order but rather interpreted thatthe first input is different than a second input. Further stepsperformed in one embodiment may also be combined with other embodiments.Thus, the specific details set forth are merely exemplary. The specificdetails may be varied from and still be contemplated to be within thespirit and scope of the present invention.

In general, a language identification engine may be described. Thelanguage identification engine includes at least the followingcomponents. A front end module having an input configured to receive anaudio stream consisting of a spoken language of at least one of a set oftwo or more potential languages being spoken in the audio stream underanalysis. A universal phoneme decoder that contains a universal phonemeset representing both 1) all phonemes occurring in the set of two ormore spoken languages, and 2) captures phoneme correspondences betweenlanguages such that a set of unique phoneme patterns is created for eachlanguage, in order to identify a most likely phoneme occurring each timein the audio stream for each of the languages in the set of two or morepotential languages in which the universal phoneme decoder was trainedon. One or more statistical language models having logic configured tosupply to a run-time language identifier module probabilities of howlinguistically likely a particular uttered phoneme identified by theuniversal phoneme decoder comes from a particular spoken language basedon an identified sequence of phonemes. The statistical model useslinguistic features from the identified phonemes from the universalphoneme decoder including the set of unique phoneme patterns created foreach language to distinguish between spoken human languages in the setof two or more spoken languages. A bank of human language specificdatabases for the one or more statistical language models to reference.Each of the databases was filled with phoneme and phoneme sequencesbeing trained on for a particular language in the set of two or morespoken languages, and each of the databases received the phoneme andphoneme sequences from a phone output from the same universal phonemedecoder independent of which spoken language in the set of two or morepotential languages was being trained on. The run-time languageidentifier module identifies a particular human language being spoken inthe audio stream in the set of two or more potential languages byutilizing the one or more statistical models. The languageidentification system that may be used with for example, a continuousspeech recognition engine that includes various components that includesfront end filters, a speech recognition decoder module, one or morestatistical language models, and an output module.

FIG. 1 illustrates a block diagram of a language identification enginein a training phase. The language ID system can be divided into twophases: training and recognition. The training phase is when variousstatistics are gathered. The run-time language identificationrecognition phase is when probability estimates, based on thesestatistics, are provided to the run-time language identification moduleon demand. During this training phase, the databases of phonemes andspecial N-gram phoneme sequences are filled/populated.

The acoustic input to the front end module coupled to the universalphoneme decoder produces a sequence of phone labels that is fed to filla bank of human language specific databases for one or more statisticallanguage models each trained to a particular human language to beidentified. In an embodiment, the training on each human language occursone language at time to maximize an accuracy of both a per-languagerecognition accuracy in identifying a correct phoneme being spoken inthat language as well as a language identification process of whichlanguage is being spoken.

During training [or even run time], the user interface 108 of thelanguage identification system has an input to receive the suppliedaudio files from a client machine over the wide area network and supplythe supplied audio files to the front end filters 110. Note the inputcould equally as well come from a live microphone or other similardevice. The training phase involves presenting the system with examplesof speech from a variety of languages. A set of languages will betrained on for example a set of 3-10 languages will be trained on andthe universal phoneme decoder will contain a universal phoneme set tocover all or most of the trained on languages.

The speech recognition front-end filters and phoneme decoder 110 convertthe supplied audio file of a continuous voice communication into atime-coded sequence of sound feature frames for speech recognition. Thefront end filters 110 filter out the background noise from the audiofile, analyze the sounds within the audio file to discrete phonemes (asknown and referred herein as phones as well) and assign a common timecode to the audio sounds occurring in supplied file. The front-endfilters 110 also transform the audio sounds into a sequence of soundfeature frames, which include sound vectors, which in essence capturethe data vectors of the sounds. The supplied audio file is time coded.The common time line may be measured in microseconds, audio frames,video frames, or some other measure of time. The multidimensional soundfeature frames that include sound data vectors come out of the front endfilters 110 at a regular interval. Thus, the front end filters 110output the time coded sequence of sound feature frames that includesound data vectors at a regular interval to supply the same soundfeature frames for analysis.

In an embodiment, when a person speaks, vibrations in the air can becaptured as an analog signal. The analog signal may be the suppliedaudio file. An analog-to-digital converter (ADC) translates this analogwave into digital data that the engine can understand. To do this, thefront end filters 110 sample, or digitize, the sound by taking precisemeasurements of the wave at frequent intervals. The front end filters110 filter the digitized sound to remove unwanted noise, and sometimesto separate it into different bands of frequency (as differences inhuman pitch). The front end filters 110 also normalize the sound, oradjust the sound to a constant volume level. The sound signal may alsohave to be temporally aligned. People do not always speak at the samespeed, so the sound must be adjusted to match the speed of the templatesound samples already stored in the system's databases. The system mayuse these coded sounds as sound feature frames.

The universal phoneme decoder 112 uses a “universal phoneme” analysisverses a “specific language” phoneme analysis. The universal phonemedecoder contains a universal phoneme set representing both 1) allphonemes occurring in a particular set of languages, and 2) capturesphoneme correspondences between languages such that a set of uniquephoneme patterns is created for each language. The unique phonemesand/or phoneme sequences may only occur in that language or in a fewlanguages, and on the other end of the spectrum, the unique phonemeand/or phoneme sequence may occur so often/with such a high occurrencerate in a particular language compared to other languages that theoccurrence of this phoneme accompanied by multiple occurrences of thisphoneme occurring within a short set time period is also a goodindicator at identifying that a particular language is being spoken. Theuniversal phoneme set in the universal phoneme decoder 112 for eachlanguage in the set of human languages will most likely contain phones,phoneme sequences, and/or a combination of both.

Thus, the set of fundamental sounds that make up a spoken languagediffer from one to the other spoken language. There will be some commonacoustic sounds between two languages whilst others will be different.These fundamental sounds are phonemes. Each language therefore will havea set of unique phoneme patterns as well as common phoneme patternscompared to other languages. The run-time language identifier module 218queries the one or more statistical language models cooperating with thehuman language specific databases 116 filled in the training process toobserve enough phoneme sequences that correspond to spoken audio so thatthe language identifier should be able to identify the spoken languageby utilizing these statistical models 216.

For example, differences exist in the statistics of phonemes in onespoke language compared to other spoken languages:

The most apparent differences between some languages are that some soundpatterns are unique to a single or just a few spoken languages. However,even in some languages that have similar sounds: the consonant space ismore discrete than the vowel space, so there is less scope for small andnon-meaning-bearing distinctions within; the phoneme inventories of thecompared languages show that, while they have significantly differentvowel inventories, their consonant inventories overlap greatly; whilevowels were considered to occur one per syllable (i.e. long vowels anddiphthongs were treated as single vowels), unlike vowels consonants canoccur in clusters at either the beginning or end of syllables; unlikevowels, consonants can be lost altogether in some languages; and othersimilar acoustic differences do exist.

Next, the universal phoneme decoder 112 may have audio attribute filtersthat are based upon decomposing digitized speech into its phoneticconstructs. The phonetic sequence is then analyzed in conjunction withacoustic model and statistical probabilities to calculate which is themost probable phoneme in the acoustic data being analyzed.

In an embodiment, the audio attribute filters use neural networktechnology and “Hidden Markov Models” (HMMs) to construct an acousticmodel that is able to provide a fast, accurate and dynamic solutionwithin variable and rapidly changing acoustic environments. The audioattribute filters recognize human speech and logs every spoken wordagainst a common time reference such as a time clock indication or videoframe.

The sound signal is divided into small segments as short as a fewhundredths of a second, or even thousandths in the case of plosiveconsonant sounds—consonant stops produced by obstructing airflow in thevocal tract—like “p” or “t.” The phoneme decoder 112 then matches thesesegments to known phonemes in the appropriate language. A phoneme is thesmallest element of a language—a representation of the sounds we makeand put together to form meaningful expressions. There are roughly 40phonemes in the English language (different linguists have differentopinions on the exact number), while other languages have more or fewerphonemes.

The phoneme decoder 112 may compare the sound pattern of each phoneme toa set of phoneme models to recognize the sound feature frames as asequence of phonemes and identifies each phoneme to that database ofknown phonemes. The phone output of the phoneme decoder 112 supplieseach identified phoneme in the series of identified phonemes to theinput of the language ID trainer 114.

Note, the universal phoneme decoder 112 may assign a symbolic code toeach phoneme based upon recognition of the phonemes from a predeterminedset. A database as part of the universal phoneme decoder may contain astandard waveform representation of each phoneme from the predeterminedset.

Overall, the training phase includes the phoneme decoder 112 tokenizingmessages in each language (i.e. converting them into identified phones),the language ID trainer 114 analyzing the resulting phones and the phonesequences, and then the language ID trainer 114 fills the language IDparameter databases 116 for the probability model for each language on aper language basis. The phoneme sequence information is modeled in thestatistical language model using discrete Markov models (HMMs). The useof a universal phoneme decoder 112 applied to each language, as opposedto a phone decoder being specifically trained to the language beingtested, allows a more consistent output from the received audio datainput. The statistical language models 216 tend to predict the correctlanguage with consistent data rather than with data that is moreaccurate.

The model for the statistics of the phones and phone sequences has beencomputed based on the output from the universal phoneme decoder 112.N-grams are basically sub-sequences of n symbols (phones in this case),and we count their occurrences. During training, the statisticallanguage models accumulate a set of n-gram sequences of phonemeshistograms, one per language, in an assumption that different languageswill have different n-gram histograms. The language ID trainer 114 thenapproximates the n-gram distribution as the weighted sum of theprobabilities of the n-gram sequence of phonemes and supplies this backto the statistical language model for that language. In essence thestatistical language model compares both the ratios of counts of phonesequences observed in the training data compared to 1) how oftenparticular phonemes and phoneme sequences are used in that humanlanguage, such as French, to an occurrence of other phoneme and phonemesequences in that human language, and 2) how often particular phonemesand phoneme sequences are used in that human language, such as French,to an occurrence of the same or very similar sounding phonemes andphoneme sequences are used in another human language, such as English.

As discussed, the run-time language identifier module 218 cooperatingwith the bank of statistical language models using the filled databases216 observes enough phoneme sequences that correspond to the spokenaudio that the run-time language identifier module 218 should be able toidentify the spoken language by utilizing these statistical models 216.

The language ID trainer module 114 analyzes the training speech audiodata for each language, and language ID parameter databases 116 for oneor more statistical language models are populated. Each of theselanguage ID parameter databases 116 for one or more statistical languagemodels are intended to represent some set of language-dependent,fundamental characteristics of the training speech that can be used inthe second (recognition) phase of the identification process. During thetraining phase, the set of language ID parameters for each language inthe set of languages are trained separately.

The language ID parameters database 116 is trained/filled with phonemesequences for each spoken language. Sequences of phonemes unique to oneor a few languages are identified. Phonemes patterns common to manydifferent languages are also identified. The set of phonemes unique toone or a few languages may include phonemes and phoneme sequences thatoccur essentially only in those one or few languages as well as phonemesand phoneme sequences that occur common to many languages but occur socommonly in those one or few languages that a high count of thosephoneme or phoneme sequences occurrence is also a good indication thatparticular language is being spoken in the audio file under analysis.

As discussed, the statistical models 216 need training so there is atraining phase in the design of the system to fill the databases 116 ona per human language basis. Each time the databases 116 being trained onone of the set of human languages receive the phone output from the sameuniversal phoneme decoder 112 independent of which human language basisis being trained on. Thus, the same universal phoneme decoder 112identifies the most likely phoneme sequence in the audio stream for eachof the languages being trained on. The language ID trainer 114 putsphones and phone sequences into a language ID parameter database 116 forthat spoken language being trained on. Each statistical model 216 hasits own spoken language specific database full of phones and phonesequences for that spoken language. Each statistical model analyzes anamount of different phones and phone sequences that occur in a trainingaudio data and counts of a total number of phonemes for the trainingaudio data upon which the model is based on. A statistical inferencemethodology uses the extracted phoneme sequence to do the languageidentification. The statistical model uses the linguistic featuresincluding the set of unique phoneme patterns to distinguish betweenspoken human languages. The statistical model may use Phonotactics arethe language-dependent set of rules specifying which phonemes areallowed to follow other phonemes. Each statistical language model 216couples to the run-time language identification 218. Each statisticallanguage model 216 provides probability estimates of how linguisticallylikely a sequence of linguistic items are to occur in that sequencebased on an amount of times the sequence of linguistic items occurs intext and phrases in general use in that spoken language. Assuming anexample trigram language model where the Ngram sequence is threelinguistic items, when queried with a context of phones xy and a phone zthat may immediately follow that context, the statistical language model208 can return an estimate P(z|xy) of the probability that z does followxy in a given language. The statistical language model 216 providesprobability estimates P(z|xy) for how linguistic likely the givensequence of phones xyz come from one of the set of spoken languages. Thestatistical language model then provides probability estimates P(z|xy)of how likely it is that specific phoneme z (or other linguistic unitssuch as a words or phone sequences) also comes from one of the set ofspoken languages based on the number of times those phone sequences andothers occur in the audio files on which the model has been trained. Thestatistical language model 216 supplies to the language identifiermodule 218 probabilities of how linguistically likely a particularuttered phoneme comes from a particular spoken language based on anidentified sequence of a phonemes.

The human language specific database 116 couples to the language IDtrainer module 114. The human language specific database 116 acts as arepository to store language ID parameters including all specialN-grams, sequences of linguistic items, that have significantlydifferent counts/occurrences in the corpus of human language specificacoustic data analyzed than would be expected compared to otherlanguages. The special N-grams (for example xyz) are linguistic items inthat sequence and are stored along with the actual counts of the numberof times that N-gram appeared in the corpus of human language specificacoustic data analyzed.

The language ID parameters database 116 couples to the run-time languageidentifier module 218. The language ID parameters database 116 is apopulated database specific to a linguistic domain that contains atleast the number of counts that the sequence of phones x followed by yoccurs in the overall corpus of human language specific acoustic dataanalyzed from this domain analyzed C(xy), as well as the number ofcounts C(xyz) the N-grams (xyz), phone sequences of x followed by yfollowed by z, occurs in the overall corpus of domain-specific acousticdata from this analyzed domain. The language ID parameters database 116returns the linguistic sequences of xy, the N-gram (xyz), and theobserved counts of both C(xy) and C(xyz) in the corpus of human languagespecific acoustic data analyzed when requested by the run-time languageID module 218. The linguistic sequences and the associated count datacreated from the analysis is stored in the language ID parametersdatabase 116 to form a language ID parameters database 116 of N-gramsfor a specific domain. Depending on size requirements, the language IDparameters database 116 and the other databases described below may eachbe implemented as simple in-memory lookup tables, as relationaldatabases on disk, or with any other standard technology.

The set of languages trained on as discussed above may be two or more.However, more typically the set of languages for which the universalphoneme decoder contains a universal phoneme set representing phonemesoccurring in the set of languages will be five or more languages. Thus,the set of language will be five or more languages.

FIG. 2 illustrates a block diagram of a language identification enginein a run-time recognition phase. During the run-time languageidentification phase, the language ID parameters for each language to beidentified are loaded into the run-time language identifier module 218.During the identification phase, a new utterance is compared to each ofthe language-dependent models 216, and the likelihood that the languageof the utterance matches the languages used to train the models iscalculated by the run-time language identifier module 218. Thelanguage-dependent statistical language model 216 most likely to becorrect is then selected by the run-time language identifier module 218.The universal phoneme decoder 212 is used to identify the phones in theaudio data covering a set of two or more possible languages to beidentified.

The identification process may be as follows:

1) The front-end 210 converts the received audio stream into time codedfeature frames for language identification, as discussed above for thetraining phase.

2) A universal phoneme decoder 212 recognizes the feature frames as asequence of phonemes, together with start/end time associated with eachfeature frame, as discussed above for the training phase. The universalphoneme detector 212 is configured to identify all of the phonemesuttered in each of the set of languages to be identified.

3) The run-time language identifier module 218 receives the phonemesequence from the universal phoneme decoder 212 in the time codedfeature frames and determines the most probable spoken language based onthe language identifying algorithm making use of the set of uniquephoneme patterns to a given spoken language verses the common phonemesequences across the different languages. As discussed above, the uniqueset of phoneme patterns includes phonemes and phonemes sequences uniqueto various languages in the set of languages, some phonemes and phonemessequences statistically uncommon to various languages in the set oflanguages but have another linguistic factor to make them statisticallyrelevant, and some phonemes and phonemes sequences that arestatistically common to various languages in the set of languages butbecause of the occurrence rate of those phonemes and phonemes sequencesbeing statistically different in a particular language and when thatoccurrence rate is compared to the sequences of phonemes being analyzed,then those common phonemes and phonemes sequences are very indicative aparticular language being spoken. The run-time language identificationmodule 218 is configured to attempt to automatically identify the spokenlanguage from a set of two or more potential languages based on phonemesequence patterns.

As discussed, a threshold value (t) may be established to set asignificant statistical amount of occurrence of similar phone and phonesequences between spoken languages to become part of the set of uniquephoneme patterns to a given spoken language. The amount can be set by auser and derived through a sequence of steps and essentially determineswhether the statistical language models are consistent or not with theevidence available to the correction module. Thus, the threshold value(t) can be an established criterion that may include a sequence of steps(perhaps) based on a statistical test to create the threshold value (t).In an embodiment, the threshold value (t) is derived from beingdiscrepant with the counts of the items concerned observed in a corpusrepresentative of the domain, where the definition of ‘discrepant’ is amatter of implementation, but will usually involve the use of astatistical test of the likelihood of those counts given the generalmodel's probability estimate. When a significant statistical amount ofoccurrence of similar phone and phone sequences occurs, then thedetermination of which language is being spoken may occur on a muchfaster basis.

4) The language identification algorithm in the run-time languageidentifier module 218 may be a second order discrete Markov model with adialogue structure and branch logic. The language identificationalgorithm in the run-time language identifier module 218 uses the secondorder Markov Model algorithm based on phoneme sequences. Recognitioninvolves tokenizing the audio data, and calculating the likelihood thatits phone sequence was produced in each of the languages. Again, thelanguage yielding the highest likelihood is identified and selected. Thelanguage may be identified using the set of unique phoneme patterns in asingle recognition pass through the system. Because the phonemes aretime annotated in a coded file, the results of the languageidentification algorithm allows the user to automatically identifysections of audio as belonging to a particular spoken language andannotate where in the audio file these transitions occur. The languageidentification algorithm is also more robust to environmentalconditions. The language ID model herein may be a multilingual speechrecognition system, where multiple languages are being spoken in thesame audio data being analyzed.

FIG. 3 illustrates a block diagram of a continuous speech recognitionengine. The continuous speech recognition engine 100 at least includesfront-end filters and phoneme decoder 102, a speech recognition decodermodule 104, general-corpus statistical language model 108, a run-timecorrection module 106, an output module of the speech recognition system110, and a user interface 112.

In an embodiment, the parts of the speech recognition system operatesimilar to the already described language identification system.

The speech recognition decoder module 104 receives the time-codedsequence of sound feature frames from the front-end filters 102 as aninput. The speech recognition decoder module 104 applies a speechrecognition processes to the sound feature frames. The speechrecognition decoder module 104 recognizes the sound feature frames as aword in a particular human language and sub dialect of that humanlanguage. The speech recognition decoder module 104 then associatesthese language parameters with the recognized word, together with astart and end time as the recognized word outputted from the speechrecognition decoder module 104. The speech recognition decoder module104 determines at least one or more best guesses at each recognizableword that corresponds to the sequence of sound feature frames. Thespeech recognition decoder module 104 supplies the one or more bestguesses at the identified word resulting from the speech recognitionprocess to the general-corpus statistical language model 108 via arun-time correction module 106.

In an embodiment, the speech recognition decoder module 104 may be anystandard speech recognition tool that outputs its one or more bestguesses as an identified/recognized word that corresponds to the worduttered in the audio file. The speech recognizer decoder module 104 maybe a complete speech recognition tool that includes a mixture Gaussiandistributions of context clustered triphones, with statistical languagemodels, and a Viterbi algorithm and/or use a Hidden Markov Model andneural networks.

The output module of the speech recognition system 110 is configured toprovide a representation of what uttered sounds and words were inputtedinto the speech recognition system based on the domain correctedprobability estimates.

Overview of Another Example Embodiment

FIG. 4 illustrates an embodiment of a continuous speech recognitionengine with a language identification engine that improves an accuracyof probability estimates. In an embodiment, the continuous speechrecognition engine 400 may include one or more inputs 402 forinformation streams, an index control module 404, a continuous speechrecognition engine including a correction module and a decoder module406, one or more attribute filters 408, 409, 410, 412, 414 in the adecoder module 406, storage devices such as a rational data base 416 andan audio-visual media server 418, an intelligence engine 420, atriggering and synchronization module 422 including an index controluser interface, and a manipulation module 424 including a query controluser interface 430. The continuous speech recognition engine 400cooperates with the language identification engine 444. The languageidentification engine 444 is hosted on a server and operates asdescribed above.

The continuous speech recognition engine 400 can be used by a user froma client machine 450 supplying audio files, including audio visualfiles, from the client machine 450 over a wide area network, such as theInternet, to a server hosting the continuous speech recognition engine400 with the robustness measure system. Examples of continuous voicecommunications are audio files of phone conversations, audio files ofradio and television shows, and other continuous flowing spoken wordsfiles. In continuous voice communications, two separate words may beuttered as close enough in time to confusion a recognition system intohaving decide whether a single word or two discrete words where in factuttered. The continuous speech recognition engine 400 automaticallyidentifies the spoken language in, for example, a phone call and thenaccurately recognizes the words being spoken with one of the languagespecific continuous speech recognition modules 408-412. The continuousspeech recognition engine 400 also automatically identifies the spokenlanguage of a media file and categorizes them.

Overall, in an embodiment, one or more streams of audio information passthrough the continuous speech recognition module 406 discussed above.The recognition modules 406 couples to the index control module 404. Theindex control 404 sends data corresponding to attributes of theinformation stream passing through the continuous speech module 406indexes all of the data from the continuous speech recognition module406. The index control module 404 then may send the indexed data to astorage device 416 as well as the intelligence engine 420. Themanipulation module 424 contains a graphic user interface 430 to allow auser to manipulate the indexed data. The triggering and synchronizationmodule 422 allows the user to program events to occur automaticallybased upon the indexed data passing through the index control module404.

In an embodiment, the continuous speech recognition engine 400 may haveone or more information stream inputs 402 into the continuous speechrecognition module 406. In an embodiment, at least one of theinformation stream inputs 402 includes audio-visual data.

The continuous speech recognition module 406 with the correctedprobability estimates translate the supplied audio and create a timecoded text file, where each transcribed word has the robust confidencelevel parameter as a measure of how confident the system is that theword was correctly identified. Each word in the supplied audio file isstored in a memory with a robust confidence level parameter and thestart and stop time codes from the common time line. The engine mayperform this function on other linguistic items including phoneme andphoneme sequences.

Accordingly, the user interface 430 may supply a transcript ofrecognized words in which those recognized words below a thresholdrobust confidence level are indicated in the transcript. Theintelligence engine 420 may assign a higher weight to recognized wordswith a robust confidence level above a threshold than recognized wordsbelow the threshold, and use the weight for the recognized words whenqueries are made with the user interface 430. The user interface 430 isconfigured to allow a speech data analytics on each word in the suppliedaudio file 402 stored in the memory based on the robust confidence levelparameter.

For example, a user from a client machine 450 may then supply to theuser interface 430 query words of interest to find out if the suppliedaudio file 402 contains any of the query words. The intelligence engine430 identifies recognized words below a certain robust confidence levelto be filtered out from the query or just placed in a hierarchical ranklist at the bottom of the ranked list due to the weighting associatedwith the recognized words below a certain robust confidence level. Theuser may then activate/click a link to the returned time segmentscontaining those recognized words matching the query words and listen toa segment of the audio file pertinent to when those words are spoken inthe supplied audio file 402.

Similarly, the continuous speech recognition engine 400, which may beresident on the server, can also monitor call center audio conversationsand identify when certain words of interest are spoken with thetriggering and synchronization module 422. The triggering andsynchronization module 422 then directs a user on the client machine 450to the time segment containing those words matching the trigger wordsand allow the user to listen to a segment of the audio file pertinent towhen those trigger words are spoken in the supplied audio file. Thetriggering and synchronization module 422 may send an event notificationto the client machine 450 over the network so the user on the clientmachine 450 can activate/click on the notification to allow the user tolisten to the segment of the audio file pertinent to when those triggerwords are spoken in the supplied audio file 402.

The continuous speech recognition module 406 cooperates with varioushuman language models 408, 410, 412, and 414, which the correctionmodule adapts to those domains. For example, an embodiment may containattribute filters including a various human language models includingUnited States English 408, United Kingdom English 410, European Spanish409, Colombian Spanish 412, and an audio sound attribute filter 414. Inan embodiment, the one or more attribute filters 408, 409, 410, 412, 414may identify attributes from each stream of information. The identifiedattributes may be a human language type, a change in human language typebeing spoken, a human accent, a change in human accent being spoken,speaker's individual voice characteristic, a change of speaker, discretespoken words, individual written words, and other similarcharacteristics. The different human language models are compared atapproximately the same time to generate a robust confidence rating foreach recognized phoneme.

In an embodiment, a human language and accent attribute filter consistsof four language models 409-412 receive the audio information stream 402to compare the output from the different human language models 409-412at approximately the same time to generate a robust confidence ratingfor each recognized word. The four exemplary human language models are aU.S. English language model 410, a U.K. English language model 411,European Spanish language model 408, and a Colombian Spanish languagemodel 412. The human language models 409-412 may be resident on the samemachine or networked across multiple machines. The audio informationstream 402 may be originally from an unstructured source such as phoneconversation. The exemplary audio information stream 402 is phoneconversation between two unknown speakers. FIG. 5 illustrates a graph ofthe continuous speech recognition engine monitoring and transcribing thephone conversation. In U.S. English, a first speaker states the words,“Is that correct.” In European Spanish, a second speaker responds withthe words, “No mas!”

The engine generates a confidence rating 560 from each language model509-512 for each spoken word over time. In an embodiment, each languagemodel 509-512 generates a confidence factor in the accuracy of eachspoken word. For the spoken word “Is” both the U.S. English languagemodel 510 and the U.K. English language model 511 have high confidencefactors 518, 520 in identifying the spoken word; however, because of thephonetic emphasis during the pronunciation of the word, “Is” the U.S.English language model 510 generates a higher confidence rating 518. Inan embodiment, a comparison between similar language models may be usedto determine the accent of the speaker. The European Spanish languagemodel 509 and the Colombian Spanish language model 512 generate a verylow confidence rating 522, 524 in the accuracy of identifying the word“Is” because that phonetic sound doesn't equate to an actual word in theSpanish language. The four language models 510, 511, 509, 512 continuedetecting and identifying the spoken words “That” and “Correct?” and dueto the individual speaker characteristics assign various confidenceratings to the identified words.

A speaker change occurs. A significant change in the value of theconfidence rating of a particular language model can be detected.Further, the attribute filter may detect and log a crossover ofconfidence ratings between the confidence rating from a first languagemodel that was higher and is now lower than a confidence rating from asecond language model. The attribute filter may make use of all thecaptured data to determine if a speaker change occurs such as pauses,confidence rating crossovers, significant changes in the value of theconfidence rating.

The second speaker states “No mas!” The four language models 510, 511,509, 512 generate medium confidence ratings 518, 520, 522, 524 on theaccuracy of the spoken word “No” because the word “No” has a meaning andtherefore is recognized in all four language models 510, 511, 509, 512.However, the European Spanish language model 509 generates the highestconfidence rating 522 due to the phonetic emphasis during thepronunciation of the word, “No.” In an embodiment, a moving time framewindow may be employed to capture the confidence factors of words spokenin the same sentence or context as the identified word in order toinfluence the confidence factor assigned to the identified word. Whenthe Spanish spoken word “mas” is pronounced, then the confidence ratingsof the English language models 518, 520 lower and the confidence ratingof the European Spanish language model 522 due to the accent increasesto again be the highest confidence rating. The captured data may be usedto generate an accurate transcript of the conversation.

Further, the captured data may be used to identify the unique voicecharacteristics of the first speaker and second speaker. For example,the first speaker may possess the unique voice characteristics ofspeaking English with a U.S. accent as well as when the speakerannunciates “Correct?” the confidence rating of a U.S. English languagemodel 518 and European Spanish language model 522 increase while theconfidence rating of a UK English language model 520 lowers. In anembodiment, the one or more attribute filters generate a time codedrecognized word with a corresponding robust confidence rating in realtime. Thus, a triggering and synchronization module may generate anevent, such as an alarm, when an attribute filter detects a recognizedword on a watch list.

Referring back to FIG. 4, for each recognized word, the attribute filtergenerates an individual XML document including as elements theidentified word, the confidence rating from each language model, and thetime code for that word. The transcript of the entire supplied audiofile corresponds with an overall XML document for that conversation.However, because each word is a discrete XML document itself within theaggregate XML document, then a user may select a phrase or portionwithin the transcript and start playing the audio segment from thatexact moment in time corresponding to the selected phrase.

The manipulation-module 424 interacts with the storage devices 416 andthe intelligence engine 420 to allow a user to navigate and utilize anindexed stream of recognized words. Transmodal manipulations of eachtype of attribute may occur due to the recognized words organizedthrough a time ordered index. A user from a client machine 450 throughthe user interface 430 may perform operations on a first set ofattributes in order to manipulate a second set of attributes.

For example, a user may create a new audio clip of a desired segment ofa radio broadcast by highlighting the transcript text and cutting thetranscript text from the text document. Further, the user may splicemultiple video clips together by assembling and inserting textcorresponding to each video clip. Thus, the user manipulates a firsttype of attribute such as the transcripted text in order to perform anoperation on the second type of attribute such as spoken words or videocharacteristics.

Also the continuous speech recognition engine 400 may be used to analyzea live audio feed. While a live feed is being broadcast, the languageidentification engine of the continuous speech recognition engine 400may receive the audio stream for analysis from a live audio source. Livebroadcast typically have a five to ten second delay between beingrecorded to being broadcast.

The continuous speech recognition engine 400 identifies each phonemewith the universal phoneme decoder to identify the language being spokenfrom an audio stream of a live broadcast as well as detecting andidentifying a new language being spoken within the same audio stream,supplying the identified language and identified phonemes to a speechrecognition module, and subsequently identifying each word in theidentified language with the speech recognition module from the audiostream of the live broadcast.

The continuous speech recognition engine 400 encodes each of theidentified phonemes and identified words from the audio stream of thelive broadcast.

The continuous speech recognition engine 400 assigns a time indicationwith each of the identified words, where each of the identified wordsshares a common time reference such as frame numbers or millisecondsinto the broadcast.

The continuous speech recognition engine 400 generating a synchronizedlink to relevant material based on the content of the live broadcast,the synchronized link to be displayed with the live broadcast. Thecontinuous speech recognition engine 400 synchronizes the synchronizedlink to appear at approximately an utterance of the most relevant wordrelated to the content of the live broadcast. The link to relevantmaterial based is generated upon the one or more words being spoken andsynchronizing a display of the link in less than ten seconds fromanalyzing the audio stream of the live broadcast.

In natural language and Boolean language queries, the intelligenceengine 420 queries a natural language and/or Boolean language query fromthe manipulation-module 424 against any part of the XML documents storedin the storage, within the intelligence engine 420, and/or storagedevices 416 external to the system such as the Internet. Theintelligence engine 420 also can be queried to provide suggestions ofsimilar content. Thus, for example, a user may remember three key wordsabout a video segment of information that the user is trying to locate.The user may submit the query through the query control user interface430 and view the resulting video segments that match the query resultson in the display window 444.

In concept matching, the intelligence engine 420 accepts a piece ofcontent or reference (identifier) as an input and returns references toconceptually related items ranked by relevance, or contextual distance.This may be used to generate automatic hyperlinks between pieces ofcontent. Thus, while a live feed is being broadcast, the triggering andsynchronization module may display hyperlinks to related documents tothe topic which the speaker is talking about based upon concept matchingto the indexed transcript correlating to the video segment.

In agent creation, the intelligence engine 420 accepts a piece ofcontent and returns an encoded representation of the concepts, includingeach concept's specific underlying patterns of terms and associatedprobabilistic ratings. In agent retraining, the intelligence engine 420accepts an agent and a piece of content and adapts the agent using thecontent. In agent matching, the intelligence engine 420 accepts an agentand returns similar agents ranked by conceptual similarity. This may beused to discover users with similar interests, or find experts in afield. This may also be used to identify a particular speaker eventhough continuous speech recognition engine 400 has no previousknowledge of that speaker.

The robust confidence level assigned to each recognized word outputtedfrom the continuous speech recognition engine 400 may be used in allsort of speech to text applications. Words below a certain robustconfidence level may be filtered out from the query or just place in ahierarchical rank list at the bottom, and identified words with a highrobust confidence level would be at the top of the hierarchical ranklist of matching words to the query. This hierarchical ranked list basedon robust confidence level in effect creates a pre-filter for the usermaking the query by ranking the more likely less relevant correspondingmatches at the bottom of the list and the more likely relevant matcheswith the higher weighed values at the top of this list reported back tothe user. The continuous speech recognition engine 400 allows the userto prioritize and moderate the search results based on robustness. Thecontinuous speech recognition engine 400 allows different weightings tobe applied to words based on robustness ratings during speech dataanalytics. The robustness rating may be used as a measure of howusable/reliable each word produced is.

The computing system environment 400 where a server hosts the continuousspeech recognition engine is only one example of a suitable computingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the invention. The invention isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well knowncomputing systems, environments, and/or configurations that may besuitable for use with the invention include, but are not limited to,personal computers, server computers, hand-held or laptop devices,multiprocessor systems, microprocessor-based systems, set top boxes,programmable consumer electronics, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

The continuous speech engine may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thoseskilled in the art can implement the description and/or figures hereinas computer-executable instructions, which can be embodied on any formof computer readable media discussed below. In general, the programmodules may be implemented as software instructions, Logic blocks ofelectronic hardware, and a combination of both. The software portion maybe stored on a machine-readable medium and written in any number ofprogramming languages such as C+, XML, HTML, etc. Therefore, thecomponent parts, such as the decoder module 106, etc. may be fabricatedexclusively of hardware logic, hardware logic interacting with software,or solely software.

A machine-readable medium includes any mechanism that stores informationin a form readable by a machine (e.g., a computer). For example, amachine-readable medium includes read only memory (ROM); random accessmemory (RAM); magnetic disk storage media; optical storage media; flashmemory devices; Digital VideoDisc (DVD's), EPROMs, EEPROMs, FLASHmemory, magnetic or optical cards, or any type of media suitable forstoring electronic instructions.

Some portions of the detailed descriptions above are presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like. These algorithms may be written in a numberof different software programming languages. Also, an algorithm may beimplemented with lines of code in software, configured logic gates insoftware, or a combination of both.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussions, itis appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers, or other suchinformation storage, transmission or display devices.

While some specific embodiments of the invention have been shown, theinvention is not to be limited to these embodiments. The invention is tobe understood as not limited by the specific embodiments describedherein, but only by scope of the appended claims.

1. A language identification engine, comprising: a front-end modulehaving an input configured to receive an audio stream that correspondsto at least one of a set of two or more candidate languages being spokenin the audio stream under analysis; a universal phoneme decoder thatcontains a universal phoneme set that 1) represents all phonemesoccurring in the set of two or more candidate languages, and 2) capturesphoneme correspondences across languages, such that a set of uniquephoneme patterns and probabilities are calculated in order to identify amost likely phoneme occurring for phonemes in the audio stream in theset of two or more candidate languages; one or more statistical languagemodels having logic configured to supply to a run-time languageidentifier module probabilities of how linguistically likely aparticular uttered phoneme identified by the universal phoneme decodercomes from a particular candidate language based on an identifiedsequence of phonemes; wherein the run-time language identifier moduleidentifies a particular human language being spoken in the receivedaudio stream from the set of two or more candidate languages byutilizing the one or more statistical language models, which have beentrained by the universal phoneme decoder; and wherein the modules makingup the language identification engine are implemented in electroniccircuitry, software coding, and any combination of the two, whereportions implemented in software coding are stored in an executableformat by a processor on a non-transitory machine-readable medium. 2.The language identification engine of claim 1, wherein the run-timelanguage identifier module queries the one or more statistical languagemodels cooperating with human language specific databases filled in atraining process to observe enough phoneme sequences that correspond tospoken audio so that the run-time language identifier is able toidentify the candidate language by utilizing the one or more statisticalmodels, and training of the statistical language models by the universalphoneme decoder on the human languages was performed one language at atime to fill each corresponding human language specific database toincrease an accuracy of both a per-language recognition accuracy inidentifying a correct phoneme being spoken in that language as well as alanguage identification process of which candidate language is beingspoken.
 3. The language identification engine of claim 1, wherein theuniversal phoneme decoder during a training phase is configured toconvert uttered sounds in supplied audio training data into resultingphonemes in each candidate language, and a language ID trainer coupledto the universal phoneme decoder analyzes the resulting phonemes andphoneme sequences, and the language ID trainer is configured to fillhuman language specific databases used by the one or more statisticallanguage models for each candidate language on a per language basis. 4.The language identification engine of claim 1, wherein the universalphoneme decoder during a training phase is applied to each candidatelanguage in the set of two or more candidate languages in order toidentify phonemes and phoneme sequences, and a consistent output fromthe universal phoneme decoder in identifying phoneme and phonemesequences is achieved for any of the audio streams under analysis duringruntime due to the training coming from the same universal phonemedecoder, as opposed to a phoneme decoder being specifically trained tothe candidate language being tested, during the training phase.
 5. Thelanguage identification engine of claim 1, wherein the one or morestatistical language models phonemes and phoneme sequences identified bythe universal phoneme decoder in the training phase are modeled usingdiscrete Markov models.
 6. A method to identify spoken words in a humanlanguage with a language identification engine, comprising: receiving anaudio stream that includes a spoken language of at least one or moreunidentified human languages being spoken in the audio stream underanalysis; identifying a most likely phoneme occurring each time in theaudio stream under analysis with a universal phoneme decoder that 1)contains a universal phoneme set representing phonemes occurring in aset of two or more spoken languages, and 2) captures phonemecorrespondences across languages, such that a set of unique phonemepatterns and probabilities are calculated; generating as an output fromthe universal phoneme decoder one or more streams of identified phonemeswith associated confidence ratings on an accuracy of the identification,where a first stream of identified phonemes is customized to at leastone of 1) a particular spoken human language and 2) a specific dialectof a spoken human language, and the first stream contains one or moreestimations for identifying the spoken phonemes in that particularspoken human language or specific dialect along with a confidencerating, where a second stream of identified phonemes is customized to atleast one of 1) a particular spoken human language and 2) a specificdialect of a spoken human language, and the spoken human language ordialect chosen for the second stream is different than the first stream;and at run-time, identifying a most likely particular human languagebeing spoken in the received audio stream in the one or more streams ofphonemes outputted from the universal phoneme decoder by utilizinglinguistic probabilities supplied by one or more statistical languagemodels that are based on the set of unique phoneme patterns created foreach language by the universal phoneme decoder.
 7. The method of claim6, further comprising: identifying the most likely phoneme sequence inthe audio stream for each of the human languages and dialects beingtrained on with the universal phoneme decoder, where the universalphoneme decoder during a training phase outputs phonemes and phonemesequences for that language or dialect being trained on and thosephonemes and phoneme sequences are stored into an associated humanlanguage specific database.
 8. The method of claim 6, furthercomprising: converting the received audio stream into time coded featureframes for language identification, wherein the universal phonemedecoder recognizes the time coded feature frames as a sequence ofphonemes, together with start/end time associated with each featureframe, and a universal phoneme detector identifies the phonemes utteredin each of a set of languages to be identified.
 9. The method of claim8, further comprising: supplying the run-time language identifier modulewith the phoneme sequence from the universal phoneme decoder in the timecoded feature frames, and determining a most probable candidate languagebased on a language identifying algorithm making use of a set of uniquephoneme patterns to each candidate language versus using a commonphoneme sequence across different candidate languages.
 10. The method ofclaim 6, further comprising: loading language identification parametersfor each candidate language to be identified into the run-time languageidentifier module during the run-time language identification phase,where a new utterance is compared to two or more of the candidatelanguage-dependent statistical models, and a likelihood that a spokenlanguage of uttered phonemes and phoneme sequences matches the candidatelanguages used to train the candidate language-dependent statisticalmodels is calculated by the run-time language identifier module.
 11. Themethod of claim 10, further comprising: selecting one or more candidatelanguages as a match to the unknown language being spoken in the audiostream under analysis based on the calculation.
 12. The method of claim6, further comprising: identifying the most likely phoneme occurringeach time in the audio stream under analysis for two or more of thecandidate languages in which the universal phoneme decoder was trainedon and the first stream has the most likely phoneme for that humanlanguage and the second stream has the most likely phoneme for thathuman language.
 13. A system including a continuous speech recognitionengine hosted on a server that cooperates with a language identificationengine in order to improve an accuracy of probability estimates,comprising: an input to receive supplied audio files from a clientmachine over a wide area network to the server hosting the continuousspeech recognition engine; and wherein the language identificationengine at least includes a front end module having an input configuredto receive the supplied audio files that include a spoken language of atleast one of a set of two or more candidate languages being spoken inthe supplied audio files under analysis, a universal phoneme decoderthat 1) contains a universal phoneme set representing the phonemesoccurring in the set of two or more spoken languages, and 2) capturesphoneme correspondences between languages, such that a set of uniquephoneme patterns and probabilities are calculated in order to identify amost likely phoneme occurring each time in the audio files in the set oftwo or more candidate languages in which the universal phoneme decoderwas trained on, one or more statistical language models having logicconfigured to supply to a run-time language identifier moduleprobabilities of how linguistically likely a particular uttered phonemeidentified by the universal phoneme decoder comes from a particularspoken language based on an identified sequence of phonemes, and wherethe run-time language identifier module is configured to identify fromthe set of two or more candidate languages at least one of 1) aparticular spoken human language and 2) a specific dialect of a spokenhuman language being spoken in the supplied audio files by utilizing thelinguistic probabilities supplied by the one or more statisticallanguage models that are based on the set of unique phoneme patternscreated for each language by the universal phoneme decoder, wherein themodules making up the language identification engine are implemented inelectronic circuits, software coding, and any combination of the two,where portions implemented in software coding are stored in anexecutable format by a processor on a non-transitory machine-readablemedium.
 14. The system of claim 13, further comprising: a set of two ormore human language specific databases for the one or more statisticallanguage models to reference, where the databases were filled withphoneme and phoneme sequences being trained on for a particular languagein the set of two or more candidate languages, and each of the humanlanguage specific databases received the phoneme and phoneme sequencesfrom a phoneme output from the universal phoneme decoder independent ofwhich spoken language in the set of two or more candidate languages wasbeing trained on.
 15. The system of claim 13, wherein a languageidentification algorithm in the run-time language identifier module is asecond order discrete Markov model with a dialogue structure and branchlogic, and the language identification algorithm uses the second orderMarkov Model algorithm based on a set of phoneme and phoneme sequencesassociated with a particular language.
 16. The system of claim 13,further comprising: a query input to receive query words of interestfrom a user of the client machine to a user interface of the continuousspeech engine to find out when the supplied audio file contains any ofthe query words, and an intelligence engine identifies recognized wordsfrom the query and returns a hierarchical rank list of recognized words.17. The system of claim 13, further comprising: a triggering andsynchronization module of the continuous speech recognition engine thatanalyzes call center audio conversations and identifies when certainwords of interest are spoken with the triggering and synchronizationmodule, wherein the triggering and synchronization module directs a useron the client machine to a time segment containing those words matchingthe words of interest and allows the user to listen to a segment of thesupplied audio files associated with when those words of interest arespoken in the supplied audio files.
 18. A computing device assistedmethod to identify spoken words in a human language with a languageidentification engine, comprising: receiving in the languageidentification engine an audio stream that corresponds to at least oneof a set of two or more candidate languages being spoken in the audiostream under analysis; identifying a most likely phoneme occurring eachtime in the audio stream under analysis with a universal phoneme decoderthat uses a universal phoneme set that 1) represents all phonemesoccurring in the set of two or more candidate languages, and 2) capturesphoneme correspondences across languages, such that a set of uniquephoneme patterns and probabilities are calculated in order to identify amost likely phoneme occurring for phonemes in the audio stream in theset of two or more candidate languages; supplying a run-time languageidentifier module probabilities of how linguistically likely aparticular uttered phoneme identified by the universal phoneme decodercomes from a particular candidate language based on an identifiedsequence of phonemes from one or more statistical language models; usinga run-time language identifier module in the language identificationengine to identify a particular human language being spoken in thereceived audio stream from the set of two or more candidate languages byutilizing the one or more statistical language models, which have beentrained by the universal phoneme decoder; and wherein any modules makingup the language identification engine are implemented in electroniccircuitry, software coding, and any combination of the two, whereportions implemented in software coding are stored in an executableformat by a processor on a non-transitory machine-readable medium.